Dynamic brain networks in motor imagery-based BCI
Résumé
Using the interactions between brain regions has great potential as new features to discriminate between mental tasks for brain computer interface (BCI). Network approaches applied to electroencephalographic (EEG)-derived functional connectivity has been recently used to identify discriminating brain organizational features in offline classification scenarios. However how those network properties temporally vary during the task, is still poorly understood. A contrario, the dynamics of event related desynchronization/synchronization resulting from local power spectra is widely known and used for online motor imagery-based BCIs. Here, we explored the offline time-frequency properties of dynamic brain networks in two subjects performing three sessions of MI-BCI for the control of a robotic arm. Results were compared to standard time-frequency power spectra and discussed in light of future implementation for online scenarios.
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